Bridging macroeconomic data between statistical classifications: the count-seed RAS approach

Abstract

In applications, it is often necessary to link heavily aggregated macroeconomic datasets adhering to different statistical classifications. We propose a simple data reclassification procedure for those cases in which a bridge matrix grounded in microdata is not available. The essential requirement of our approach, which we refer to as count-seed RAS, is that there exists a time period or a geographical entity similar to the one of interest for which the relevant economic variable is observed according to both classifications. From this information, a bridge matrix is constructed using bi-proportional methods to rescale a seed matrix based on a qualitative correspondence table from official sources. We test the procedure in two case studies and by Monte Carlo methods. We find that, in terms of reclassification accuracy, it performs noticeably better than other expeditious methods. The analytical framework underlying our approach may prove a useful way of conceptualizing data reclassification problems.

Linking multisectoral economic models and consumption surveys for the European Union

“…We also found that while using an official table as a prior, even if randomly selected, it outperforms recent methods such as the count-seed RAS Vandyck, 2020 andRueda-Cantuche, 2019) and significantly outperforms the naïve prior.…”

“…An alternative would have been constructing them based on the Eurostat (2018b) correspondence. • Count-seed RAS: Cai and Vandyck (2020) recently estimated contingency tables for the European Union using the count-seed RAS method (Cai & Rueda-Cantuche, 2019) in which a single benchmark matrix is constructed by counting the number of items that simultaneously contribute to a given aggregate cell of the table in a disaggregated mapping between CPA and COICOP (3 000 CPA categories and more than 100 COICOP ones); then a RAS is used until convergence.…”